Spiral: Shaping the New Era in Customer Support Intelligence

Executive Summary

This whitepaper comprehensively examines Spiral, a groundbreaking customer support tool tailored for enterprise-level operations. Spiral sets itself apart by harnessing the capabilities of advanced Large Language Models (LLMs) and Artificial Intelligence (AI), establishing new benchmarks in the analysis of customer interactions.

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The whitepaper also delves into the diverse features and functionalities of Spiral. Each feature is meticulously designed to enhance the analytical capabilities of businesses, ensuring more precise, comprehensive, and timely insights into customer interactions.

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Furthermore, this whitepaper explains the Analytics Maturity Model and its importance, illustrating how Spiral assists companies in progressing through various stages of analytics maturity – from fundamental data comprehension to prescriptive and proactive analytics. This progression is vital for businesses to evolve from simply understanding historical data to actively shaping customer experiences and trends.

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This whitepaper aims to position Spiral as a transformative tool in the field of customer support analytics. Spiral is not merely an analytical instrument but a strategic partner that empowers businesses to leverage the ability of AI for insightful analysis of customer interactions and enhanced decision-making. Readers are encouraged to explore Spiral further and contemplate how its cutting-edge features can redefine their approach to customer support analytics, ultimately driving significant business growth and improving customer satisfaction.

Product Overview: Spiral

Spiral’s primary goal is to set a global benchmark in customer interaction analysis, with a remarkable capability to process multiple languages and dialects. Central to Spiral's design is its proficiency in gathering and analyzing substantial volumes of unstructured data from various customer feedback & support sources. It stands out for its ability to process and interpret complex, multilingual customer conversations efficiently, offering a comprehensive view of customer experiences and issues critical for in-depth analysis.

Strategic Impact

Spiral goes beyond mere data collection and analysis. It makes these insights actionable by linking them directly to key business metrics. This integration enables businesses to understand and act on customer issues effectively, enabling strategic decision-making. The transformation of raw data into practical strategies marks a significant advancement over traditional analytics methods, providing businesses with an unmatched tool to enhance customer experience, optimize operations, and promote business growth.

Competitive Advantage

What truly sets Spiral apart is its unique AI approach, which surpasses the capabilities of traditional platforms like Medallia, NICE, and Qualtrics. Spiral's AI is not a generic tool; it's a precision instrument, finely tuned and personalized to the specific nuances of a company's customer support data. This distinction allows for rapid, accurate issue detection and analysis, turning what used to be weeks of data processing into minutes.

The technology's ability to drill down into the specifics of customer support data gives Spiral a significant edge in the market. Its system is not about collecting keywords but understanding each customer interaction's context and specificities. This deep, nuanced understanding of customer issues and trends makes Spiral a formidable tool in the arsenal of any customer-centric business.

Key Features and Functionalities

Omnichannel Issue Identification: Spiral elevates analytics capabilities by autonomously identifying and classifying customer issues across various support channels. This eliminates manual data entry, leading to a more accurate and comprehensive understanding of customer interactions.
Rapid Root Cause Analysis: Spiral empowers businesses to swiftly ascertain the root causes of issues reported by customers. This drastically cuts down the time and labor typically needed for manual analysis, offering immediate and accurate insights into the factors affecting customer interactions.
Proactive Early Warning System: Spiral's innovative early warning system proactively identifies emerging patterns and issues that are not yet on the company's radar. This ensures timely recognition and resolution of potentially significant problems.
Advanced KPI Attribution: Spiral represents a significant advancement in proactive business analytics by continuously monitoring customer interactions and alerting businesses to issues that negatively impact key performance indicators.
Comprehensive Data Integration: Spiral ensures smooth integration with most CRM systems, cloud contact centers, and customer feedback platforms, maintaining an uninterrupted flow of data for a complete overview of customer interactions.
Multilingual Support: Spiral's capacity to process and understand feedback in 75 languages makes it an ideal solution for global businesses, overcoming language barriers in feedback analysis.
Customizable Dashboards: Spiral provides dynamic, user-friendly dashboards tailored to each organization's and team’s specific needs, offering instant insights into customer interactions and trends.
Real-Time Data Processing: The platform continuously updates its dashboard as often as it receives data, offering a real-time perspective on customer issues and trends, and allowing businesses to respond promptly to evolving customer feedback.

Exploring Spiral's Cutting-Edge Technology

01. Advanced AI Technology

Spiral's technological backbone is anchored in its advanced AI capabilities, primarily by deploying Large Language Models (LLMs) and Artificial Intelligence. These technologies are at the forefront of Spiral's data processing and analytics prowess, enabling it to handle complex customer interactions with remarkable precision.

Enabling Agents to Provide Information

Spiral's use of LLMs represents a significant leap in natural language processing (NLP). These models, trained on vast datasets, excel in interpreting and understanding natural language on an unprecedented scale.
The LLMs in Spiral are designed to discern nuances in customer language, interpret sentiments, and extract meaningful insights from any unstructured data source.
The integration of these models allows Spiral to transform raw customer interaction data into structured, actionable intelligence, facilitating a deeper understanding of customer needs and behavior patterns.

Generative AI

Beyond analysis, Spiral employs Generative AI to aid with the identification of the smallest & newest issues that arise in customer datasets. When there are not enough patterns for our general ML to detect an issue, we boost the training data and data sampling with GenAI, resulting in faster and more precise issue identification.

02. Data Security and Privacy

Spiral prioritizes data security and privacy, incorporating robust protocols and compliance measures to ensure the integrity and confidentiality of customer data.

Robust Security Protocols

Spiral’s architecture includes advanced encryption standards, safeguarding data both during transit and at rest. This ensures protection against data breaches and unauthorized access.
Regular security audits and continuous monitoring systems are in place to promptly detect and mitigate potential vulnerabilities.

Privacy Measures

Compliance with global data protection regulations such as GDPR and CCPA is integral to Spiral’s operation. This compliance underscores its commitment to handling customer data responsibly and ethically.
Spiral adheres to privacy-by-design principles, ensuring user privacy is a cornerstone of its data-handling practices.

SOC2 Type II Certification

Spiral has an up-to-date SOC2 Type II certification, evidencing its adherence to the highest standards for security, availability, processing integrity, confidentiality, and customer data privacy.

PII Removal

Spiral was built to identify trends in customer support interactions, and PII doesn't provide any needed trends. As a result, Spiral has mastered instant PII removal – we eliminate all PII when we get any data from our customers. PII removed by default includes email addresses, phone numbers, SSNs, account numbers, and date of birth.

03. Integration Capabilities

Spiral's integration process is designed to integrate existing business systems seamlessly. The platform's integration capabilities, particularly with CRM systems like Salesforce, Zendesk, or even simpler via S3 bucket drops, facilitate a smooth transition and daily scanning of customer interactions.

CRM System Integration

Integrates with leading CRM & UCaaS systems, enabling direct data ingestion and processing. This integration streamlines the workflow, allowing for real-time analysis of customer interactions.
Offers customizable integration options to align with CRM systems' specific business processes and data structures.

Flexible and Customizable Integration

It provides a modular integration framework adaptable to different business needs and technical environments. This flexibility ensures that Spiral can be tailored to suit various operational contexts and technical infrastructures.

04. Scalability and Performance

Designed for high scalability and optimized performance, Spiral can handle evolving business demands and large-scale data processing with ease.

Highly Scalable Architecture

Spiral’s architecture is built to efficiently manage large volumes of diverse data, scaling dynamically as business data requirements grow.
The system is designed to accommodate increases in data volume without compromising on processing speed or analytics accuracy.

Optimized for Performance

Engineered for high-speed data processing, Spiral delivers real-time analytics and an early warning system, ensuring that businesses have access to the latest insights for prompt decision-making.
The platform is optimized for minimal latency in both data processing and dashboard updates, enhancing the user experience and operational efficiency.

Spiral's combination of advanced AI technologies, stringent data security measures, and robust integration capabilities positions it as a highly reliable and effective tool for businesses that leverage sophisticated analytics in customer support and experience management. Its scalable, high-performance architecture ensures that Spiral remains a powerful asset for businesses navigating the evolving landscape of customer interaction and data analysis.

Spiral's Innovative Approach to Machine Learning Model Training

Spiral is revolutionizing how businesses understand and categorize customer support interactions through cutting-edge Large Language Models (LLMs) and a dynamic, data-driven training process. Our technology is not confined to traditional machine learning methodologies that rely heavily on pre-labeled datasets. Instead, we employ a novel approach that combines the power of LLMs with the unique data provided by our clients, ensuring the delivery of custom solutions tailored to the specific needs of each business.

Data Collection and Transformation

Our process begins with comprehensive data collection, encompassing customer conversations across various channels (such as live chats and phone calls, surveys, reviews, and essential metadata – including customer geography, segment, plan, product type, and release version). We also consider any existing categorization the client provides, although this is optional.

Our goal is to convert all collected data into a standardized Spiral schema. This uniformity allows us to treat every client's data with the same analytical precision, offering the additional benefit of creating a clean data lake for our clients.

The Spiral Three-Step Process to Data Insight

Specific Issue Discovery: Utilizing a blend of LLMs and specific training on the client's support data, we develop a custom model for each client. This model is finely tuned to detect the most nuanced customer issues relevant to their business. Our approach ensures a robust detection of issues across all provided channels, identifying both common and unique issues specific to each medium.
Categorization Alignment: We align issues with the client’s needs once issues are identified. This step can take various forms:
- Utilizing an existing taxonomy the client prefers.
- Merging and refining multiple taxonomies provided by the client, supplemented with new categories discovered by Spiral's AI.
- Creating a completely new taxonomy based on the issues identified by Spiral.
Accuracy and Coverage Boost: We evaluate and enhance the accuracy of issue tagging, especially if the client has previously tagged conversations. We aim to expand coverage beyond typical industry standards, using a mix of existing and new labels identified by Spiral to improve data comprehension and utility.

Beyond Traditional Machine Learning

Our approach transcends conventional machine learning paradigms. Rather than merely clustering data into predefined categories, Spiral's methodology is flexible, adapting to each client's unique requirements. Whether it involves enhancing an existing taxonomy, discovering new categories, or starting from scratch without predefined labels or categories, Spiral's AI-driven system is equipped to handle these diverse scenarios with precision and efficiency.

This flexible, innovative approach ensures that our clients benefit from a machine-learning solution that is not just about fitting data into buckets but about creating a deep, actionable understanding of customer interactions. Spiral's technology suite, anchored by LLMs and enriched through client-specific data training, positions our platform at the forefront of customer support analytics. This unique blend of technology and client-centric customization sets Spiral apart, enabling businesses to unlock unprecedented insights into customer needs and behaviors, thus driving strategic decision-making and enhancing customer experiences.

Technical Solutions and Positioning

Our technical solutions are as diverse as our client needs. We employ various technologies and methodologies, such as clustering, to cater to different scenarios. Our approach resembles a radar chart, balancing different aspects (categories, labels, new issue discovery) to achieve a unified and comprehensive solution.

Regardless of the starting point, Spiral's ML model training process is designed to meet clients where they are and guide them to a balanced and effective solution. We aim to develop a unified taxonomy, accurate labeling, and robust categories, ensuring a holistic and efficient machine-learning model tailored to each business's unique needs.

The Analytics Maturity Model:
Your Journey to Customer Understanding

Defining Analytics Maturity

The need for advanced analytics has never been more critical. As businesses grow and customer interactions become increasingly complex, traditional customer feedback analysis methods are no longer sufficient. The digital age has ushered in an era where customer data is not only abundant but also multifaceted. However, harnessing this data to derive meaningful and actionable intelligence remains a significant challenge for many organizations.

Analytics Maturity refers to the degree to which an organization can effectively and efficiently use data analytics to inform decision-making and drive business strategy. It's a journey that moves from basic data awareness to advanced, predictive, and prescriptive analytics. The maturity model serves as a framework, helping organizations understand their current capabilities and chart a path to more sophisticated analytics practices.

Stages of Analytics Maturity and Their Outcomes

Descriptive Analytics - What Happened?
- The first stage involves basic data collection and reporting.
- Organizations at this level focus on answering questions about historical data.
- Outcome: Improved understanding of past performance.
Diagnostic Analytics - Why Did It Happen?
- This stage goes deeper into data to understand causes and effects.
- Involves more sophisticated data examination like drilling down, data discovery, and correlations.
- Outcome: Ability to explain why certain events occurred.
Predictive Analytics - What Could Happen?
- Involves forecasting future outcomes based on historical data.
- Uses statistical models and machine learning to predict future trends.
- Outcome: Better preparation for future scenarios and risk management.
Prescriptive Analytics - What Should We Do About It?
- The most advanced stage combines intelligence from all previous stages.
- Uses complex algorithms and machine learning to recommend actions.
- Outcome: Data-driven decision-making and strategic planning.
Proactive Analytics - How Can We Make It Happen?
- Organizations at this stage continuously innovate based on analytics.
- Uses real-time data and AI to anticipate and shape future outcomes actively.
- Outcome: Leading market trends, setting industry benchmarks.

Examples of Fully Mature Analytical Organizations

Fully mature analytical organizations are those that have reached the proactive analysis stage. They are characterized by their ability to not only react to but also anticipate and even shape customer trends. These organizations often:

Use real-time analytics to drive daily operations.
AI to predict and influence customer behaviors.
Have a culture where data-driven decisions are the norm, not the exception.

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Current Challenges: Technical and Cultural Barriers

Despite the clear advantages of advancing through the analytics maturity model, organizations face numerous challenges, both technical and cultural:

Technical Challenges:

Lack of infrastructure to support advanced analytics.
Difficulty in integrating diverse data sources.
Gaps in skills or technology needed for advanced data analysis.

Cultural Barriers:

Resistance to change within the organization.
A mindset that undervalues data-driven decision-making.
Difficulty in transitioning from traditional decision-making processes to ones driven by data analytics.

Addressing these challenges requires a combination of strategic planning, investment in technology and talent, and a cultural shift toward valuing and understanding data analytics. Spiral, with its AI-driven approach, is uniquely positioned to guide organizations through this transformative journey, helping them overcome both technical and cultural barriers.

How Spiral Helps Companies Achieve Analytics Maturity

Spiral plays a crucial role in guiding companies through each stage of analytics maturity, helping them evolve from basic data understanding to advanced, actionable intelligence.

Descriptive Analytics

"What Happened?"

At this foundational stage, companies seek to understand historical data to grasp what has happened in the past. Spiral aids in this by aggregating and centralizing data and providing early warning detection from various sources, offering a comprehensive overview of past and current customer interaction trends. This broad perspective is essential for companies to understand their historical performance, especially regarding customer feedback and experiences.

For example, Spiral detected that duplicate payment submissions started yesterday at 4 pm.

Diagnostic Analytics

"Why It Happened?"

The next stage involves delving deeper into the data to discern the reasons behind past events. Spiral's advanced AI-driven analysis is pivotal here, as it helps businesses explore the reasons behind customer behaviors and feedback. By identifying underlying patterns and causes, Spiral provides insights into why certain trends have emerged.

For example, Spiral detected that most users whose payment was processed twice happened to use Klarna as the payment partner and were located in the US.

Predictive Analytics

"What Is Likely to Happen?"

Predictive analytics moves beyond understanding the past to forecasting future outcomes. Spiral leverages historical and current data trends to predict future customer behaviors and issues. This foresight enables businesses to anticipate and prepare for upcoming events or challenges, aligning strategies proactively.

For example, since Spiral has already observed the payment issue, it checked similar issues over the years. Now, it can predict the potential upcoming agent load that would need to happen unless the company mitigates the issue.

Prescriptive Analytics

"What Should We Do About It?"

Prescriptive analytics represents a more advanced stage, where the focus shifts from predicting future trends to guiding actions. In this context, Spiral significantly enhances the capability of support organizations by equipping them with deep insights that enable them to offer informed suggestions.

For example, when anticipating issues with future payments, support teams are empowered to proactively gather pertinent information to create a comprehensive FAQ section specifically addressing the anticipated payment problems. Additionally, they can identify any recent updates or releases that might be causing these issues, ensuring that their guidance is both relevant and timely.

Proactive Analytics

"How Can We Make It Happen?"

Spiral utilizes historical and current data to help companies predict future trends and customer behaviors, empowering businesses to foresee and influence future outcomes. Its ability to identify emerging patterns enables companies to anticipate and address challenges or opportunities ahead of time, enhancing strategic planning and resource allocation.

For example, by observing the times of the year, typical agent loads, and relevant customer complaints, support leaders can propose future agent staffing numbers and provide a heads-up even on the low-grade issues that might become potential problems over the next few weeks.

Spiral Case Study

Company Overview

Background and Mission

Founded in 2018 by Elena Zhizhimontova and Andrew DiLosa, Spiral emerged from the vision of creating an all-encompassing solution for customer feedback management. As former software engineers at Amazon, the co-founders experienced the immense challenges of handling vast volumes of customer feedback data, including thousands of bug reports and user issues daily. This firsthand experience highlighted the need for a more efficient, systematic approach to identifying and addressing customer feedback.

Motivated by the desire to streamline this process, Elena and Andrew embarked on an extensive research journey, interviewing over 175 companies to understand the breadth of this challenge. Their findings confirmed a widespread need for a tool that not only identifies customer issues but also delves into their root causes, offering swift and effective solutions.

Spiral was born from this need. It stands as the first AI-powered system designed to thoroughly analyze, pinpoint root causes, and prioritize customer issues across support, customer experience, and product departments. Its innovative approach enables organizations to conduct extensive issue research and root cause analysis, focusing on quick resolution and proactive problem management.

Spiral's inception marks a significant advancement in customer feedback analysis, offering businesses a comprehensive, AI-driven solution to enhance their customer interaction management and decision-making processes.

About the Founders

Elena Zhizhimontova: Co-Founder and CEO of Spiral

Elena Zhizhimontova, serving as the Co-Founder and CEO, is a dynamic force in the technological landscape. Her tenure at Amazon as part of the FireTV team equipped her with valuable insights into the complexities of managing customer feedback. This experience was pivotal in the conceptualization of Spiral.

Elena's impressive academic credentials include a Master's in Computer Science from Cornell University and a Bachelor of Arts from Clark University. Her leadership at Spiral reflects her commitment to transforming customer feedback management. She has been instrumental in developing strategies that not only sift through extensive customer feedback but also enhance the overall customer experience and business efficacy.

Andrew DiLosa: Co-Founder and CTO of Spiral

Andrew DiLosa, as the Co-Founder and Chief Technology Officer, brings a wealth of experience and innovative insight to Spiral. His journey in the tech world began on the Amazon FireTV team, where he honed his skills in software development and algorithm creation, particularly for enhancing user experiences in movie and TV show recommendations. It was during his tenure at Amazon that Andrew recognized the crucial need for a deeper understanding of customer feedback - a realization that laid the groundwork for Spiral.

His educational background from the University of Maryland, where he earned a degree in Computer Science, complements his professional journey. Andrew's innovative approach to addressing customer issues at Spiral is a testament to his dedication to enhancing customer satisfaction through technology.

Shaping the New Era in Customer Support Intelligence